To count the number of records, you should be able to use tf.python_io.tf_record_iterator. c = 0 for fn in tf_records_filenames: for record in tf.python_io.tf_record_iterator(fn): c += 1 To just keep track of the model training, tensorboard comes in handy.
The function _floats_feature described in the Tensorflow-Guide expects a scalar (either float32 or float64) as input. def _float_feature(value): “””Returns a float_list from a float / double.””” return tf.train.Feature(float_list=tf.train.FloatList(value=[value])) As you can see the inputted scalar is written into a list (value=[value]) which is subsequently given to tf.train.FloatList as input. tf.train.FloatList expects an iterator that outputs … Read more
Just for clarity, I have a few thousand images in a single .tfrecords file, they’re 720 by 720 rgb png files. The labels are one of 0,1,2,3. I also tried using the parse_example and couldn’t make it work but this solution works with the parse_single_example. The downside is that right now I have to know … Read more
I hope this helps: filename_queue = tf.train.string_input_producer([‘/Users/HANEL/Desktop/tf.png’]) # list of files to read reader = tf.WholeFileReader() key, value = reader.read(filename_queue) my_img = tf.image.decode_png(value) # use decode_png or decode_jpeg decoder based on your files. init_op = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init_op) # Start populating the filename queue. coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) for i … Read more
Found it! import tensorflow as tf for example in tf.python_io.tf_record_iterator(“data/foobar.tfrecord”): print(tf.train.Example.FromString(example)) You can also add: from google.protobuf.json_format import MessageToJson … jsonMessage = MessageToJson(tf.train.Example.FromString(example))